CharacterizeImageTask#

class lsst.pipe.tasks.characterizeImage.CharacterizeImageTask(schema=None, **kwargs)#

Bases: PipelineTask

Measure bright sources and use this to estimate background and PSF of an exposure.

Given an exposure with defects repaired (masked and interpolated over, e.g. as output by IsrTask): - detect and measure bright sources - repair cosmic rays - measure and subtract background - measure PSF

Parameters#

schemalsst.afw.table.Schema, optional

Initial schema for icSrc catalog.

**kwargs

Additional keyword arguments.

Notes#

Debugging: CharacterizeImageTask has a debug dictionary with the following keys:

frame

int: if specified, the frame of first debug image displayed (defaults to 1)

repair_iter

bool; if True display image after each repair in the measure PSF loop

background_iter

bool; if True display image after each background subtraction in the measure PSF loop

measure_iter

bool; if True display image and sources at the end of each iteration of the measure PSF loop See displayAstrometry for the meaning of the various symbols.

psf

bool; if True display image and sources after PSF is measured; this will be identical to the final image displayed by measure_iter if measure_iter is true

repair

bool; if True display image and sources after final repair

measure

bool; if True display image and sources after final measurement

Methods Summary

detectMeasureAndEstimatePsf(exposure, ...)

Perform one iteration of detect, measure, and estimate PSF.

display(itemName, exposure[, sourceCat])

Display exposure and sources on next frame (for debugging).

run(exposure[, background, idGenerator])

Characterize a science image.

runQuantum(butlerQC, inputRefs, outputRefs)

Do butler IO and transform to provide in memory objects for tasks run method.

Methods Documentation

detectMeasureAndEstimatePsf(exposure, idGenerator, background)#

Perform one iteration of detect, measure, and estimate PSF.

Performs the following operations:

  • if config.doMeasurePsf or not exposure.hasPsf():

    • install a simple PSF model (replacing the existing one, if need be)

  • interpolate over cosmic rays with keepCRs=True

  • estimate background and subtract it from the exposure

  • detect, deblend and measure sources, and subtract a refined background model;

  • if config.doMeasurePsf:

    • measure PSF

Parameters#

exposurelsst.afw.image.ExposureF

Exposure to characterize.

idGeneratorlsst.meas.base.IdGenerator

Object that generates source IDs and provides RNG seeds.

backgroundlsst.afw.math.BackgroundList, optional

Initial model of background already subtracted from exposure.

Returns#

resultlsst.pipe.base.Struct

Results as a struct with attributes:

exposure

Characterized exposure (lsst.afw.image.ExposureF).

sourceCat

Detected sources (lsst.afw.table.SourceCatalog).

background

Model of subtracted background (lsst.afw.math.BackgroundList).

psfCellSet

Spatial cells of PSF candidates (lsst.afw.math.SpatialCellSet).

Raises#

LengthError

Raised if there are too many CR pixels.

display(itemName, exposure, sourceCat=None)#

Display exposure and sources on next frame (for debugging).

Parameters#

itemNamestr

Name of item in debugInfo.

exposurelsst.afw.image.ExposureF

Exposure to display.

sourceCatlsst.afw.table.SourceCatalog, optional

Catalog of sources detected on the exposure.

run(exposure, background=None, idGenerator=None)#

Characterize a science image.

Peforms the following operations: - Iterate the following config.psfIterations times, or once if config.doMeasurePsf false:

  • detect and measure sources and estimate PSF (see detectMeasureAndEstimatePsf for details)

  • interpolate over cosmic rays

  • perform final measurement

Parameters#

exposurelsst.afw.image.ExposureF

Exposure to characterize.

backgroundlsst.afw.math.BackgroundList, optional

Initial model of background already subtracted from exposure.

idGeneratorlsst.meas.base.IdGenerator, optional

Object that generates source IDs and provides RNG seeds.

Returns#

resultlsst.pipe.base.Struct

Results as a struct with attributes:

exposure

Characterized exposure (lsst.afw.image.ExposureF).

sourceCat

Detected sources (lsst.afw.table.SourceCatalog).

background

Model of subtracted background (lsst.afw.math.BackgroundList).

psfCellSet

Spatial cells of PSF candidates (lsst.afw.math.SpatialCellSet).

characterized

Another reference to exposure for compatibility.

backgroundModel

Another reference to background for compatibility.

Raises#

RuntimeError

Raised if PSF sigma is NaN.

runQuantum(butlerQC, inputRefs, outputRefs)#

Do butler IO and transform to provide in memory objects for tasks run method.

Parameters#

butlerQCQuantumContext

A butler which is specialized to operate in the context of a lsst.daf.butler.Quantum.

inputRefsInputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined input/prerequisite connections.

outputRefsOutputQuantizedConnection

Datastructure whose attribute names are the names that identify connections defined in corresponding PipelineTaskConnections class. The values of these attributes are the lsst.daf.butler.DatasetRef objects associated with the defined output connections.